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Int. J. Mol. Sci. 2016, 17(6), 882; doi:10.3390/ijms17060882

A Nonlinear Model for Gene-Based Gene-Environment Interaction

1
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2
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3
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1,* and 1,4,*
1
Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
2
School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China
3
Department of Mathematics, San Francisco State University, San Francisco, CA 94132, USA
4
Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
*
Authors to whom correspondence should be addressed.
Academic Editor: Jianhua Zhu
Received: 8 March 2016 / Revised: 7 May 2016 / Accepted: 21 May 2016 / Published: 4 June 2016
(This article belongs to the Special Issue Gene–Environment Interactions)
View Full-Text   |   Download PDF [3484 KB, uploaded 4 June 2016]   |  

Abstract

A vast amount of literature has confirmed the role of gene-environment (G×E) interaction in the etiology of complex human diseases. Traditional methods are predominantly focused on the analysis of interaction between a single nucleotide polymorphism (SNP) and an environmental variable. Given that genes are the functional units, it is crucial to understand how gene effects (rather than single SNP effects) are influenced by an environmental variable to affect disease risk. Motivated by the increasing awareness of the power of gene-based association analysis over single variant based approach, in this work, we proposed a sparse principle component regression (sPCR) model to understand the gene-based G×E interaction effect on complex disease. We first extracted the sparse principal components for SNPs in a gene, then the effect of each principal component was modeled by a varying-coefficient (VC) model. The model can jointly model variants in a gene in which their effects are nonlinearly influenced by an environmental variable. In addition, the varying-coefficient sPCR (VC-sPCR) model has nice interpretation property since the sparsity on the principal component loadings can tell the relative importance of the corresponding SNPs in each component. We applied our method to a human birth weight dataset in Thai population. We analyzed 12,005 genes across 22 chromosomes and found one significant interaction effect using the Bonferroni correction method and one suggestive interaction. The model performance was further evaluated through simulation studies. Our model provides a system approach to evaluate gene-based G×E interaction. View Full-Text
Keywords: nonlinear gene-environment interaction; sparse principal component analysis; varying-coefficient model nonlinear gene-environment interaction; sparse principal component analysis; varying-coefficient model
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Sa, J.; Liu, X.; He, T.; Liu, G.; Cui, Y. A Nonlinear Model for Gene-Based Gene-Environment Interaction. Int. J. Mol. Sci. 2016, 17, 882.

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